A YOLO-Based Automatic Bangladeshi Vehicle License Plate Recognition System

Ashraful Islam; Naimul Amin; Ali Ahmmed; Nushrat Jahan Nishita

doi:10.5935/jetia.v12i58.3138

Authors

Ashraful Islam Department of Electrical and Electronic Engineering, Bangladesh Army International University of Science & Technology (BAIUST), Cumilla-3501, Bangladesh https://orcid.org/0000-0002-2406-1938 (unauthenticated)
Naimul Amin Department of Electrical and Electronic Engineering, Bangladesh Army International University of Science & Technology (BAIUST), Cumilla-3501, Bangladesh https://orcid.org/0009-0006-9869-809X (unauthenticated)
Ali Ahmmed Department of Electrical and Electronic Engineering, Bangladesh Army International University of Science & Technology (BAIUST), Cumilla-3501, Bangladesh https://orcid.org/0009-0001-1247-4073 (unauthenticated)
Nushrat Jahan Nishita Department of Electrical and Electronic Engineering, Bangladesh Army International University of Science & Technology (BAIUST), Cumilla-3501, Bangladesh https://orcid.org/0009-0008-0783-5090 (unauthenticated)

DOI:

https://doi.org/10.5935/jetia.v12i58.3138

Abstract

In this paper, a YOLO based automatic Bangladeshi vehicle license plate recognition system is proposed. The proposed system has four parts: license plate detection, extracting the region of interest (ROI), applying image processing to the ROI, and character segmentation & recognition. In the detection & extraction stage, the system receives a vehicle image and then detects the license plate and extracts the plate region. The recognition part consists of three consecutive stages: city name recognition, vehicle type recognition and vehicle serial number recognition. As the presented method recognizes the license plate characters in three consecutive stages, a data serialization algorithm is proposed to serial the data. The dataset contains 500 license plate images from four major cities of Bangladesh. The images are used to train, test and validate the proposed model. The proposed method has provided very impressive results and outperformed many other existing methods.